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Labeled Alignment for Recognizing Textual Entailment 6

Labeled Alignment for Recognizing Textual Entailment

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6th International Joint Conference on Natural Language Processing, IJCNLP 2013

作者： Wang, Xiao-Lin Zhao, Hai Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Department of Computer Science and Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China

ISBN: (纸本)9784990734800

Recognizing Textual Entailment (RTE) is to predict whether one text fragment can semantically infer another, which is required across multiple applications of natural language processing. The conventional alignment scheme, which is developed for machine translation, only marks the paraphrases and hyponyms to justify the entailment pairs, while provides less support for the non-entailment ones. This paper proposes a novel alignment scheme, named labeled alignment, to address this problem, which introduces negative links to explicitly mark the contradictory expressions to justify the non-entailment pairs. Thus the alignment-based RTE method employing the proposed scheme, compared with those employing the conventional one, can gain accuracy improvement through actively detecting the signals of non-entailment. The experimental results on the data sets of two shared RTE tasks indicate the implemented system significantly outperforms both the baseline system and all the other submitted systems. © IJCNLP *** right reserved.

关键词： Alignment

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Hybrid rule-based algorithm for coreference resolution

Hybrid rule-based algorithm for coreference resolution

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2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2012 - Shared Task: Modeling Multilingual Unrestricted Coreference in OntoNotes

作者： Shou, Heming Zhao, Hai Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University China

ISBN: (纸本)9781937284459

This paper describes our coreference resolution system for the CoNLL-2012 shared task. Our system is based on the Stanford's dcoref deterministic system which applies multiple sieves with the order from high precision to low precision to generate coreference chains. We introduce the newly added constraints and sieves and discuss the improvement on the original system. We evaluate the system using OntoNotes data set and report our results of average F-score 58.25 in the closed track. © 2012 Association for Computational Linguistics.

关键词： Sieves

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Emotion Recognition from Eye Movements Using Multi-way Autoregressive Model

Emotion Recognition from Eye Movements Using Multi-way Autor...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ma, Tian-Fang Liu, Xuan-Hao Zheng, Wei-Long Lu, Bao-Liang Shanghai Jiao Tong University Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China

ISBN: (纸本)9798350386226

The application of physiological signals in emotion recognition is a popular research topic in human-computer interactions. Eye movement, as an important physiological signal, plays an essential role in medicine, psychology, cognitive science, and other scientific research fields. Previous studies have successfully identified human emotions by combining various eye-related measurements, selecting features, and utilizing machine learning techniques. However, the exploration of eye movement signals in emotion recognition remains insufficient. In this study, we utilize eye tracking heatmap and eye movement trajectory data for emotion recognition for the first time. Based on the two types of eye movement data, we develop a multi-way autoregressive model capable of processing multi-view eye movement data. Compared to traditional deep learning baseline models, our model better adapts to the structure of eye movement data and significantly improves the classification performance. Furthermore, we integrate heatmap and trajectory data with commonly used eye-related measurement features, which further enhance the performance of emotion recognition beyond previous methods. © 2024 IEEE.

关键词： Emotion Recognition

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Denoising Diffusion Tensor Images with Shearlet

Denoising Diffusion Tensor Images with Shearlet

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2012 IEEE 11th International Conference on Signal Processing(ICSP 2012)

作者： Xiangfen Zhang Bao-Liang Lu Yan Ma Xiaozhong Xu Fangfang Wei Wenjie Xu Institute of Intelligent Computing & Image Processing College of Mechanical and Electronic EngineeringShanghai Normal University Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and EngineeringShanghai Jiaotong University MOE-Microsoft Key Lab for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University

ISBN: (纸本)9781467321969

Diffusion tensor imaging （DTI） is known to be the best non-invasive imaging modality in providing anatomical information as white-matter fiber bundles. However, the Gaussian noise introduced into the diffusion tensor images can bring serious impacts on tensor calculation and fiber tracking. To decrease the effects of the Gaussian noise, many denoising methods have been presented. In this paper, a shearlet based denosing strategy is introduced. To evaluate the efficiency of the proposed shearlet based denoising method in accounting for the Gaussian noise introduced into the images, the peak to peak signal-to-noise ratio （PSNR）, signal-to-mean squared error ratio （SMSE） and edge keeping index （Beta） metrics are adopted. The experiment results acquired from both the synthetic and real data indicate the good performance of our proposed filter.

关键词： diffusion tensor imaging shearlet transform wavelet transform PSNR SMSE denoising

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Detection of driving fatigue based on grip force on steering wheel with wavelet transformation and support vector machine

Detection of driving fatigue based on grip force on steering...

引用

20th International Conference on Neural Information Processing, ICONIP 2013

作者： Li, Fan Wang, Xiao-Wei Lu, Bao-Liang Center for Brain-like Computing and Machine Intelligence Department of Computer Science and Engineering China MOE-Microsoft Key Lab. for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China

ISBN: (纸本)9783642420504

This paper proposes an unobtrusive way to detect fatigue for drivers through grip forces on steering wheel. Simulated driving experiments are conducted in a refitted passenger car, during which grip forces of both hands are collected. Wavelet transformation is introduced to extract fatigue-related features from wavelet coefficients. We compare the performance of k-nearest neighbours, linear discriminant analysis, and support vector machine (SVM) on the task of discriminating drowsy and awake states. SVM with radial basis function reaches the best accuracy, 75% on average. The results show that variation in grip forces on steering wheel can be used to effectively detect drivers' fatigue. © Springer-Verlag 2013.

关键词： Support vector machines

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Personalizing EEG-based affective models with transfer learning 25

Personalizing EEG-based affective models with transfer learn...

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25th International Joint Conference on Artificial intelligence, IJCAI 2016

作者： Zheng, Wei-Long Lu, Bao-Liang Center for Brain-like Computing and Machine Intelligence Department of Computer Science and Engineering China Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering China Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China

Individual differences across subjects and nonstationary characteristic of electroencephalography (EEG) limit the generalization of affective braincomputer interfaces in real-world applications. On the other hand, it is very time consuming and expensive to acquire a large number of subjectspecific labeled data for learning subject-specific models. In this paper, we propose to build personalized EEG-based affective models without labeled target data using transfer learning techniques. We mainly explore two types of subject-to-subject transfer approaches. One is to exploit shared structure underlying source domain (source subject) and target domain (target subject). The other is to train multiple individual classifiers on source subjects and transfer knowledge about classifier parameters to target subjects, and its aim is to learn a regression function that maps the relationship between feature distribution and classifier parameters. We compare the performance of five different approaches on an EEG dataset for constructing an affective model with three affective states: positive, neutral, and negative. The experimental results demonstrate that our proposed subject transfer framework achieves the mean accuracy of 76.31% in comparison with a conventional generic classifier with 56.73% in average.

关键词： Electroencephalography

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EEG Data Augmentation for Emotion Recognition Using a Conditional Wasserstein GAN 40

EEG Data Augmentation for Emotion Recognition Using a Condit...

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40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018

作者： Luo, Yun Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai200240 China

ISBN: (纸本)9781538636466

Due to the lack of electroencephalography (EEG) data, it is hard to build an emotion recognition model with high accuracy from EEG signals using machine learning approach. Inspired by generative adversarial networks (GANs), we introduce a Conditional Wasserstein GAN (CWGAN) framework for EEG data augmentation to enhance EEG-based emotion recognition. A Wasserstein GAN with gradient penalty is adopted to generate realistic-like EEG data in differential entropy (DE) form. Three indicators are used to judge the qualities of the generated high-dimensional EEG data, and only high quality data are appended to supplement the data manifold, which leads to better classification of different emotions. We evaluate the proposed CWGAN framework on two public EEG datasets for emotion recognition, namely SEED and DEAP. The experimental results demonstrate that using the EEG data generated by CWGAN significantly improves the accuracies of emotion recognition models. © 2018 IEEE.

关键词： Electroencephalography Gallium nitride Training Generators Emotion recognition Generative adversarial networks brain modeling

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Grammatical error detection and correction using a single maximum entropy model 18

Grammatical error detection and correction using a single ma...

引用

18th Conference on Computational Natural Language Learning: Shared Task, CoNLL 2014

作者： Wang, Peilu Jia, Zhongye Zhao, Hai Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China

ISBN: (纸本)9781941643198

This paper describes the system of Shanghai Jiao Tong Unvierity team in the CoNLL-2014 shared task. Error correction operations are encoded as a group of predefined labels and therefore the task is formulized as a multi-label classification task. For training, labels are obtained through a strict rule-based approach. For decoding, errors are detected and corrected according to the classification results. A single maximum entropy model is used for the classification implementation incorporated with an improved feature selection algorithm. Our system achieved precision of 29.83, recall of 5.16 and F_0.5 of 15.24 in the official evaluation. © 2014 Association for Computational Linguistics.

关键词： Error correction

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Regression with Phrase Indicators for Estimating MT Quality 12

Regression with Phrase Indicators for Estimating MT Quality

引用

Workshop on Statistical machine Translation

作者： Chunyang Wu Hai Zhao Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University

ISBN: (纸本)9781622765928

We in this paper describe the regression system for our participation in the quality estimation task of WMT12. This paper focuses on exploiting special phrases, or word sequences, to estimate translation quality. Several feature templates on this topic are put forward subsequently. We train a SVM regression model for predicting the scores and numerical results show the effectiveness of our phrase indicators and method in both ranking and scoring tasks.

关键词： simple regression (model) Regression (Psychology) reduced mass Word MT machine translation

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A Novel Method for EOG Features Extraction from the Forehead

A Novel Method for EOG Features Extraction from the Forehead

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Hao-Yu Cai Jia-Xin Ma Li-Chen Shi Bao-Liang Lu Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University

ISBN: (纸本)9781424441211

We have shown that the slow eye movements extracted from electrooculogram (EOG) signals can be used to estimate human vigilance in our previous work. However, the traditional method for recording EOG signals is to place the electrodes near the eyes of subjects. This placement is inconvenient for users in real-world applications. This paper aims to find a more practical placement for acquiring EOG signals for vigilance estimation. Instead of placing the electrodes near the eyes, we place them on the forehead. We extract EOG features from the forehead EOG signals using both independent component analysis and support vector machines. The performance of our proposed method is evaluated using the correlation coefficients between the forehead EOG signals and the traditional EOG signals. The results show that a correlation of 0.84 can be obtained when the users make 14 different face movements and for merely eye movements it reaches 0.93.

关键词： Electrooculography Electrodes Correlation Forehead Support vector machines Electromyography Feature extraction

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